This is a followup to an earlier post about word embeddings, which capture the meaning of a word using a low-dimensional vector, and are ubiquitous in natural language processing. I will talk about my joint work with Li, Liang, Ma, Risteski, which tries to mathematically explain their fascinating properties.
We focus on a few questions. (a) What properties of natural languages cause these low-dimensional embeddings to exist? (b) Why do low-dimensional embeddings work better at analogy solving than high dimensional embeddings?
In a future blog post I will address another question answered by our subsequent work: How should a word embedding be interpreted when a word has multiple meanings?
Why do low-dimensional embeddings capture huge statistical information?
Recall that all embedding methods try to leverage word co-occurence statistics. Latent Semantic Indexing does a low-rank approximation to word-word cooccurence probabilities. In the simplest version, if the dictionary has $N$ words (usually $N$ is about $10^5$) then find $v_1, v_2, \ldots, v_N \in \mathbb{R}^{300}$ that minimize the following expression, where $p(w, w’)$ is the empirical probability that words $w, w’$ occur within $5$ words of each other in a text corpus like wikipedia. (Here “$5$” and “$300$” are somewhat arbitrary.)
Of course, one can compute a rank$-300$ SVD for any matrix; the surprise here is that the rank $300$ matrix is actually a reasonable approximation to the $100,000$-dimensional matrix of cooccurences. (Since then, topic models have been developed which imply that the matrix is indeed low rank.) This success motivated many extensions of the above basic idea; see the survey on Vector space models. We’re interested today in methods that perform nonlinear operations on word cooccurence probabilities. The simplest uses the old and popular PMI measure of Church and Hanks, where the probability $p(w,w’)$ in expression (1) is replaced by the following (nonlinear) measure of correlation. (And still the $10^5 \times 10^5$ matrix turns out to have a good low-rank approximation.)
Of course, researchers in applied machine learning take the existence of such low-dimensional approximations for granted, but there appears to be no theory to explain their existence. Theoretical explanations are also lacking for other recent methods such as Google’s word2vec.
Our paper gives an explanation using a new generative model for text, which also gives a clearer insight into the causative relationship between word meanings and the cooccurence probabilities. We think of corpus generation as a dynamic process, where the $t$-th word is produced at step $t$. The model says that the process is driven by the random walk of a discourse vector $c_t \in \Re^d$. It is a unit vector whose direction in space represents what is being talked about. Each word has a (time-invariant) latent vector $v_w \in \Re^d$ that captures its correlations with the discourse vector. We model this bias with a loglinear word production model:
The discourse vector does a slow geometric random walk over the unit sphere in $\Re^d$. Thus $c_{t+1}$ is obtained by a small random displacement from $c_t$. Since expression (2) places much higher probability on words that are clustered around $c_t$, and $c_t$ moves slowly, the model predicts that words occuring at successive time steps will also tend to have vectors that are close together. But this correlation weakens after say, $100$ steps. This model is basically the loglinear topic model of Mnih and Hinton, but with an added dynamic element in the form of a random walk. The model is also related to many existing notions like Kalman filters and linear chain CRFs. Also, as is usual in topic models, it ignores grammatical structure, and treats text in small windows as a bag of words.
Our main contribution is to use the model assumptions to derive closed form expressions for the word-word cooccurence probabilities in terms of the latent variables (i.e., the word vectors). This involves integrating out the random walk $c_t$. For this we need to make a theoretical assumption, which says intuitively that the bulk behavior of the set of all word vectors is similar to what it would be if they were randomly strewn around the conceptual space (this is counterintuitive to my linguistics colleagues because they are used to the existence of fine-grained structure in word meanings).
Isotropy assumption about word vectors: In the bulk, the word vectors behave like random vectors, for example, like $s \cdot u$ where $u$ is a standard Gaussian vector and $s$ is a scalar random variable. In particular, the partition function $Z_c = \sum_w \exp(v_w \cdot c)$ is approximately $Z \pm \epsilon$ for most unit vectors $c$.
We find that in practice the partition function is well-concentrated. After writing our paper we discovered that this phenomenon had been been discovered already in empirical work on self-normalizing language models. (Basic message: Treat the partition function as constant; it doesn’t hurt too much!)
The tight concentration of partition function allows us to compute a multidimensional integral to obtain expressions for word probabilities.
Thus the model predicts that the PMI matrix introduced earlier is indeed low-dimensional. Furthermore, unlike previous models, low dimension plays a key role in the story: isotropy requires the dimension $d$ to be much smaller than $N$.
A theoretical “explanation” also follows for some other nonlinear models. For instance if we try to do a max-likelihood fit (MLE) to the above expressions, something interesting happens in the calculation: different word pairs need to be weighted differently. Suppose you see that $w, w’$ cooccur $X(w, w’)$ times in the corpus. Then it turns out that your trust in this count as an estimate of the true value of $p(w, w’)$ scales linearly with $X(w, w’)$ itself. In other words the MLE fit to the model is
This is very similar to the expression in the GloVE model, but provides some explanation for their mysterious bias and reweighting terms. Empirically we find that this model fits the data quite well: the weighted termwise error (without the square) is about $5$ percent. (The weighted termwise error for the PMI model is much worse, around $17\%$.)
A theoretical explanation can also be given for Google’s word2vec model. Suppose we assume the random walk of the discourse vector is slow enough that $c_t$ is essentially unchanged while producing consecutive strings of $10$ words or more. Then the average of the word vectors for any consecutive $5$ words is a Max a Posteriori (MAP) estimate of the discourse vector $c_t$ that produced them. This leads to the word2vec(CBOW) model, which had hitherto seemed mysterious:
Why do low dimensional embeddings work better than high-dimensional ones?
A striking finding in empirical work on word embeddings is that there is a sweet spot for the dimensionality of word vectors: neither too small, nor too large. This graph below from the Latent Semantic Analysis paper (1997) shows the performance on word similarity tasks versus dimension, but a similar phenomenon also occurs for analogy solving.
Such a performance curve with a bump at the “sweet spot” is very familiar in empirical work in machine learning and usually explained as follows: too few parameters make the model incapable of fitting to the signal; too many parameters, and it starts overfitting (e.g., fitting to noise instead of the signal). Thus the dimension constraint act as a regularizer for the optimization.
Surprisingly, I have not heard of a good theoretical explanation to back up this intuition. Here are some attempted explanations I heard from colleagues in connection with word embeddings.
Suggestion 1: Johnson-Lindenstrauss Lemma implies some dimension reduction for every set of vectors. This explanation doesn’t cut it because: (a) it only implies dimension $\frac{1}{\epsilon^2}\log N$, which is too high for even moderate $\epsilon$. (b) It predicts that quality of the embedding goes up monotonically as we increase dimension, whereas in practice overfitting is observed.
Suggestion 2: Standard generalization theory (e.g., VC-dimension) predicts overfitting. I don’t see why this applies either, since we are dealing with unsupervised learning (or transfer learning): the training objective doesn’t have anything to do a priori with analogy solving. So there is no reason a model with fewer parameters will do better on analogy solving, just as there’s no reason it does better for some other unrelated task like predicting the weather.
We give some idea why a low-dimensional model may solve analogies better. This is also related to the following phenomenon.
Why do Semantic Relations correspond to Directions?
Remember the striking discovery in the word2vec paper: word analogy tasks can be solved by simple linear algebra. For example, the word analogy question man : woman ::king : ?? can be solved by looking for the word $w$ such that $v_{king} - v_w$ is most similar to $v_{man} - v_{woman}$; in other words, minimizes
This strongly suggests that semantic relations —in the above example, the relation is masculine-feminine—correspond to directions in space. However, this interpretation is challenged by Levy and Goldberg who argue there is no linear algebra magic here, and the expression can be explained simply in terms of traditional connection between word similarity and vector inner product (cosine similarity). See also this related blog post.
We find on the other hand that the RELATIONS = DIRECTIONS phenomenon is demonstrable empirically, and is particularly clear for semantic analogies in the testbed. For each relation $R$ we can find a direction $\mu_R$ such if a word pair $a, b$ satisfy $R$, then
where $\alpha_{a, b}$ is a scalar that’s roughly about $0.6$ times the norm of $v_a - v_b$ and $\eta$ is a noise vector. Empirically, the residuals $\eta$ do look mathematically like random vectors according to various tests.
In particular, this phenomenon allows the analogy solver to be made completely linear algebraic if you have a few examples (say 20) of the same relation. You compute the top singular vector of the matrix of all $v_a -v_b$’s to recover $\mu_R$, and from then on can solve analogies $a: b:: c:??$ by looking for a word $d$ such that $v_c - v_d$ has the highest possible projection on $\mu_R$ (thus ignoring $v_a -v_b$ altogether). In fact, this gives a “cheating” method to solve the analogy test bed with somewhat higher success rates than state of the art. (Message to future designers of analogy testbeds: Don’t include too many examples of the same relationship, otherwise this cheating method can exploit it.) By the way, Lisa Lee did a senior thesis under my supervision that showed empirically that this phenomenon can be used to extend knowledge-bases of facts, e.g., predict new music composers in the corpus given a list of known composers.
Our theoretical results can be used to explain the emergence of RELATIONS=DIRECTIONS phenomenon in the embeddings. Earlier attempts (eg in the GloVE paper) to explain the success of (3) for analogy solving had failed to account for the fact that all models are only approximate fits to the data. For example, the PMI model fits $v_w \cdot v_{w’}$ to $PMI(w, w’)$) but the termwise error for our corpus is $17\%$, and expression (3) contains $6$ inner products! So even though expression (3) is presumably a linear algebraic proxy for some statistical property of the word distributions, the noise/error is large. By contrast, the difference in the value of (3) between the best and second-best solution is small, say $10-15\%$.
So the question is: Why does error in the approximate fit not kill the analogy solving? Our explanation of RELATIONS = DIRECTIONS phenomenon provides an explanation: the low dimension of the vectors has a “purifying” effect that reduces the effect of this fitting error. (See Section 4 in the paper.)The key ingredient of this explanation is, again, the random-like behavior of word embeddings —quantified in terms of singular values— as well as the standard theory of linear regression. I’ll describe the math in a future post.


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To separate multiple URLs you can use: '\n'|' '|','|';'
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{# Template stolen from snippets service home page: https://github.com/mozmeao/snippets-service/blob/master/snippets/base/templates/base/home.jinja Thanks once again to Giorgos! -#}
made with ❤ by Paul McLanahan
{% if host == 'mozillian' %}Simply go to "<your-mozillians-username>.mozillian.dev" and you'll be taken to your profile on mozillians.org.
Problem? Suggestion? Submit an issue to the repo.
{% else %}Would you like "<yourname>.{{ host }}.dev" to redirect to your URL? Submit a pull-request to this repo.
{% endif %}


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Dev's week 6 Cultural Blog Site
Cultural Blog: Stereotype Threat
The Stereotype Threat
- I haven't personally felt the stereotype threat but can see how this may effect other groups of people. I can see how a negative self-image can cause someone to not perform to their maximum potential and cause them to underachieve.
- I think that we can try to educate minority groups who have this issue and help address the problem. We would need to present this information at schools that have a high percentage of minorities and help create a positive self-image for them. Once we help them identify this condition they can start to create strategies to overcome it.


--- layout: page title: About header-img: "img/about-bg.png" ---
30 + year IT Service Delivery and Support Professional


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Speed of Boat:
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Depth of water:
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Angular Resolution:
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Runtime:
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OUTPUTS:
Expansion Ratio:
X
Interpolated Width Path:
Rate of Survey:
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Actual Area Surveyed:
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Project: Gooooaaaal! (and Funnel)
This week we are tasked to set up goals using Google Analytics.
I thought the set up was fine it just took me a little bit of time to figure out what my goal was going to be. I used the tutorial from our class and from the Google Analytics tutorial on setting up goals and funnels.
My Goal
For this project I want to track how long people watch this video called What’s New In Web Accessibility. For roughly a half hour the presenter talks about automating web accessibility. I want to track that in my post and send it out to as many people as I can.
The presentation is about a Google project about automating web accessibility called Lighthouse. This project tests for SEO, best practices, and accessibility and has been integrated in Chrome Dev Tools. The presenter goes through an interesting walk through of his auditing of a website using Lighthouse. The video itself is 37 minutes but I want to see how long people watch the first section about Lighthouse which is about 20 minutes. I do not believe that many people will watch the full 20 minutes but I do think there is enough interesting content where a user will watch the first 5 minutes.
The second part is equally as import which is about the Accessibility Object Model, proposed as JavaScript API but for the purposes for this project I just want to focus on the first 5 minutes as pertaining to Lighthouse.
The goal I set up is called Lighthouse – Goal Duration Test and I set the details to track the goal of a duration of 5 minutes. Because it was a duration goal, I was unable to create a funnel since those are just for destination goals.
I also added an id to the link to track the events whenever someone clicked on the link as opposed to watched the video embedded on the page. I made the id of “lighthouse”.
I added the link to a couple of Slack channels though my school and am planning to send the link to friends, family and other ways to generate traffic.
Placeholder text by Space Ipsum. Photographs by NASA on The Commons.


The Purpose of a Chemical Peel
To improve the appearance of the skin, correct the appearance of facial blemishes, and to reduce the effects of smoking and sun exposure. Peels can also be used for reducing uneven pigmentation, acne and acne scarring and other skin conditions.
A chemical peel is designed to accelerate the removal of old, dead skin cells at the surface of the skin to promote new cell growth and can be used to treat a particular area (such as lines around the eyes or mouth), or all over the face, arms, hands and neck.
Types of CP On Offer
- Novita Superficial Peels
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- BHA (betahydroxy acid) Peels


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C
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E
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G
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A
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B
Major
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Minor 7th
Dominant 7th
Sus2
Sus4
C
C♯ / D♭
D
E♯ / E♭
E
F
F♯ / G♭
G
G♯ / A♭
A
A♯ / B♭
B
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Major 7th
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Dominant 7th
Sus2
Sus4
Output
Tempo
90
MIDI outputs & clock
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Source Code


![ion.rangeSlider](_tmp/logo-ion-range-slider.png)
> English description | Описание на русском
Ion.RangeSlider. Is an easy, flexible and responsive range slider with tons of options.
***
* Version: 2.1.4
* Project page and demos
* Download ZIP
## Description
* Ion.RangeSlider — cool, comfortable, responsive and easily customizable range slider
* Supports events and public methods, has flexible settings, can be completely altered with CSS
* Cross-browser: Google Chrome, Mozilla Firefox 3.6+, Opera 12+, Safari 5+, Internet Explorer 8+
* Ion.RangeSlider supports touch-devices (iPhone, iPad, Nexus, etc.).
* Ion.RangeSlider freely distributed under terms of MIT licence.
* With this plugin you will be able to build beautiful range sliders, like this:
![ion.rangeSlider](http://ionden.com/a/plugins/ion.rangeSlider/static/img/ion-range-slider.png)
## Key features
* Skin support. (5 skins included and PSD for skin creation)
* Any number of sliders at one page without conflicts and big performance problems
* Two slider types single (1 slider) and double (2 sliders)
* Support of negative and fractional values
* Ability to set custom step and snap grid to step
* Support of custom values diapason
* Customisable grid of values
* Ability to disable UI elements (min and max, current value, grid)
* Postfixes and prefixes for your numbers ($20, 20 € etc.)
* Additional postfix for maximum value (eg. $0 — $100+)
* Ability to prettify large numbers (eg. 10000000 -> 10 000 000 or 10.000.000)
* Slider writes its value right into input value field. This makes it easy to use in any html form
* Any slider value can be set through input data-attribute (eg. data-min="10")
* Slider supports disable param. You can set it true to make slider inactive
* Slider supports external methods (update, reset and remove) to control it after creation
* For advanced users slider has callbacks (onStart, onChange, onFinish, onUpdate). Slider pastes all its params to callback first argument as object
* Slider supports date and time
## Demos
* Basic demo
* Advanced demo
* Interactions demo
## Dependencies
* jQuery 1.8.x+
## Usage
Add the following libraries to the page:
* jQuery
* ion.rangeSlider.min.js
Add the following stylesheets to the page:
* normalize.css (optional)
* ion.rangeSlider.css
Plus, a skin for the slider. 5 skins are included. Choose one:
* ion.rangeSlider.skinFlat.css + sprite-skin-flat.png
* ion.rangeSlider.skinHTML5.css + no images
* ion.rangeSlider.skinModern.css + sprite-skin-modern.png
* ion.rangeSlider.skinNice.css + sprite-skin-nice.png
* ion.rangeSlider.skinSimple.css + sprite-skin-simple.png
Or use the included PSD file and design a custom skin.
## Install with bower
* bower install ionrangeslider
## Install with npm
* npm install ion-rangeslider
## Initialisation
The slider overrides a native text input
element.
```html
```
To initialise the slider, call ionRangeSlider on the element:
```javascript
$("#example_id").ionRangeSlider();
```
## Demo for juniors
If your are new in web development and you are not sure how to correctly install the plugin to your web-page, please download
this demo example
## Migrating from 1.x to 2.x
* All params (except functions) are lowercase now: param_name, not paramName
* Same param names was changed: hasGrid → grid, onLoad → onStart
* Callbacks data object format was changed. Example: fromNumber → from
* Slider now writes it's result to value attribute and also to data-from and data-to attributes
## Experiments playground
## Settings
| Option | Defaults | Type | Description |
|---|---|---|---|
| type data-type |
"single" | string | Choose slider type, could be single - for one handle, or double for two handles |
| min data-min |
10 | number | Set slider minimum value |
| max data-max |
100 | number | Set slider maximum value |
| from data-from |
min | number | Set start position for left handle (or for single handle) |
| to data-to |
max | number | Set start position for right handle |
| step data-step |
1 | number | Set sliders step. Always > 0. Could be fractional. |
| min_interval data-min-interval |
— | number | Set minimum diapason between sliders. Only in "double" type |
| max_interval data-max-interval |
— | number | Set maximum diapason between sliders. Only in "double" type |
| drag_interval data-drag-interval |
false | boolean | Allow user to drag whole range. Only in "double" type (beta) |
| values data-values |
[] | array | Set up your own array of possible slider values. They could be numbers or strings. If the values array is set up, min, max and step param, are no longer can be changed. |
| from_fixed data-from-fixed |
false | boolean | Fix position of left (or single) handle. |
| from_min data-from-min |
min | number | Set minimum limit for left handle. |
| from_max data-from-max |
max | number | Set the maximum limit for left handle |
| from_shadow data-from-shadow |
false | boolean | Highlight the limits for left handle |
| to_fixed data-to-fixed |
false | boolean | Fix position of right handle. |
| to_min data-to-min |
min | number | Set the minimum limit for right handle |
| to_max data-to-max |
max | number | Set the maximum limit for right handle |
| to_shadow data-to-shadow |
false | boolean | Highlight the limits for right handle |
| prettify_enabled data-prettify-enabled |
true | boolean | Improve readability of long numbers. 10000000 → 10 000 000 |
| prettify_separator data-prettify-separator |
" " | string | Set up your own separator for long numbers. 10 000, 10.000, 10-000, etc. |
| prettify — |
null | function | Set up your own prettify function. Can be anything. For example, you can set up unix time as slider values and than transform them to cool looking dates. |
| force_edges data-force-edges |
false | boolean | Slider will be always inside it's container. |
| keyboard data-keyboard |
false | boolean | Activates keyboard controls. Move left: ←, ↓, A, S. Move right: →, ↑, W, D. |
| keyboard_step data-keyboard-step |
5 | number | Movement step, than controling from keyboard. In percents. |
| grid data-grid |
false | boolean | Enables grid of values. |
| grid_margin data-grid-margin |
true | boolean | Set left and right grid borders. |
| grid_num data-grid-num |
4 | number | Number of grid units. |
| grid_snap data-grid-snap |
false | boolean | Snap grid to sliders step (step param). If activated, grid_num will not be used. |
| hide_min_max data-hide-min-max |
false | boolean | Hides min and max labels |
| hide_from_to data-hide-from-to |
false | boolean | Hide from and to lables |
| prefix data-prefix |
— | string | Set prefix for values. Will be set up right before the number: $100 |
| postfix data-postfix |
— | string | Set postfix for values. Will be set up right after the number: 100k |
| max_postfix data-max-postfix |
— | string | Special postfix, used only for maximum value. Will be showed after handle will reach maximum right position. For example 0 — 100+ |
| decorate_both data-decorate-both |
true | boolean | Used for "double" type and only if prefix or postfix was set up. Determine how to decorate close values. For example: $10k — $100k or $10 — 100k |
| values_separator data-values-separator |
" — " | string | Set your own separator for close values. Used for "double" type. Default: 10 — 100. Or you may set: 10 to 100, 10 + 100, 10 → 100 etc. |
| input_values_separator data-input-values-separator |
" ; " | string | Separator for double values in input value property. |
| disable data-disable |
false | boolean | Locks slider and makes it inactive. |
| onStart — |
null | function | Callback. Is called on slider start. |
| onChange — |
null | function | Callback. IS called on each values change. |
| onFinish — |
null | function | Callback. Is called than user releases handle. |
| onUpdate — |
null | function | Callback. Is called than slider is modified by external methods update or reset . |
data-*
attributes of input tag:
```html
data-min="0"
data-max="10000"
data-from="1000"
data-to="9000"
data-type="double"
data-prefix="$"
data-grid="true"
data-grid-num="10"
```
## Public methods
To use public methods, at first you must save slider instance to variable:
```javascript
// Launch plugin
$("#range").ionRangeSlider({
type: "double",
min: 0,
max: 1000,
from: 200,
to: 500,
grid: true
});
// Saving it's instance to var
var slider = $("#range").data("ionRangeSlider");
// Fire public method
slider.reset();
```
There are 3 public methods:
```javascript
// UPDATE - updates slider to any new values
slider.update({
from: 300,
to: 400
});
// RESET - reset slider to it's first values
slider.reset();
// DESTROY - destroys slider and restores original input field
slider.destroy();
```
## One more look on demos
* Basic demo
* Advanced demo
* Interactions demo
All plugins options are covered in demos.
### Update history
***
#### Support Ion-series plugins development:
* Donate through Pledgie service: [![](https://pledgie.com/campaigns/25694.png?skin_name=chrome)](https://pledgie.com/campaigns/25694)
* Donate direct to my Paypal account: https://www.paypal.me/IonDen
* Donate direct to my Yandex.Money account: http://yasobe.ru/na/razrabotku


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TowerClimber
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Game-icons.net
.


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We are a professional and up-to-date dance school in the South. We cater to all levels and ages. We hold weekly Dance Classes for both boys and girls, taught by some of the best and most experienced teachers in the area. These classes are guaranteed to transform you as a dancer and an all round performer!
• FREESTYLE • STREET • LYRICAL/SLOW DANCE • CONTEMPORARY • MUSICAL THEATRE • BABY BALLET • ACRO • FITNESS • SQUAD/COMPETITION
TEAM •
Exams, Shows and Competitions available
All uniforms can be ordered through Starr Dancewear. Dance school t-shirt and leggings must only have silver logo. Boys uniform has matt logo. All other items can have silver and pink logo if you prefer.
Crawley, Burgess Hill & Crawley Down
0787 622 3676
<EMAIL_ADDRESS>Worthing, Ferring & Lancing
07832 146 799
<EMAIL_ADDRESS>Pulborough, Billingshurst & Ashington
07552913405
katedeacon7@gmail.com


#include <tiffio.h> #define TIFFGetR(abgr) ((abgr) & 0xff) #define TIFFGetG(abgr) (((abgr) >> 8) & 0xff) #define TIFFGetB(abgr) (((abgr) >> 16) & 0xff) #define TIFFGetA(abgr) (((abgr) >> 24) & 0xff) int TIFFReadRGBAImage(TIFF* tif, u_long width, u_long height, u_long* raster, int stopOnError)
Raster pixels are 8-bit packed red, green, blue, alpha samples. The macros TIFFGetR, TIFFGetG, TIFFGetB, and TIFFGetA should be used to access individual samples. Images without Associated Alpha matting information have a constant Alpha of 1.0 (255).
TIFFReadRGBAImage converts non-8-bit images by scaling sample values. Palette, grayscale, bilevel, CMYK, and YCbCr images are converted to RGB transparently. Raster pixels are returned uncorrected by any colorimetry information present in the directory.
The paramater stopOnError specifies how to act if an error is encountered while reading the image. If stopOnError is non-zero, then an error will terminate the operation; otherwise TIFFReadRGBAImage will continue processing data until all the possible data in the image have been requested.
Samples must be either 1, 2, 4, 8, or 16 bits. Colorimetric samples/pixel must be either 1, 3, or 4 (i.e. SamplesPerPixel minus ExtraSamples).
Palettte image colormaps that appear to be incorrectly written as 8-bit values are automatically scaled to 16-bits.
TIFFReadRGBAImage is just a wrapper around the more general TIFFRGBAImage(3T) facilities.
Sorry, can not handle %d-bit pictures. The image had BitsPerSample other than 1, 2, 4, 8, or 16.
Sorry, can not handle %d-channel images. The image had SamplesPerPixel other than 1, 3, or 4.
Missing needed "PhotometricInterpretation" tag. The image did not have a tag that describes how to display the data.
No "PhotometricInterpretation" tag, assuming RGB. The image was missing a tag that describes how to display it, but because it has 3 or 4 samples/pixel, it is assumed to be RGB.
No "PhotometricInterpretation" tag, assuming min-is-black. The image was missing a tag that describes how to display it, but because it has 1 sample/pixel, it is assumed to be a grayscale or bilevel image.
No space for photometric conversion table. There was insufficient memory for a table used to convert image samples to 8-bit RGB.
Missing required "Colormap" tag. A Palette image did not have a required Colormap tag.
No space for tile buffer. There was insufficient memory to allocate an i/o buffer.
No space for strip buffer. There was insufficient memory to allocate an i/o buffer.
Can not handle format. The image has a format (combination of BitsPerSample, SamplesPerPixel, and PhotometricInterpretation) that TIFFReadRGBAImage can not handle.
No space for B&W mapping table. There was insufficient memory to allocate a table used to map grayscale data to RGB.
No space for Palette mapping table. There was insufficient memory to allocate a table used to map data to 8-bit RGB.


| 3 Feb 1903 |
Charles Arthur "Pretty Boy" Floyd born, Bartow Co., GA. |
| 1921 |
Marries Wilma (Ruby) Hargrove. |
| 11 Sep 1925 |
Assists in the robbery of a payroll, $11,500. For this, he is sentenced to prison. |
7 Mar 1929 |
Released from prison. |
| 1931 |
Robs 51 banks in Oklahoma. |
| 15 Jan 1932 |
Oklahoma Bankers Association posts a $6000 reward for Floyd's capture. |
| 17 Jun 1933 |
Four policemen are killed by associates of Frank Nash while attempting to break Nash out of custody; among them J. Edgar Hoover incorrectly identifies Floyd. "No time, money or labor will be spared toward bringing about the individuals responsible for this cowardly, despicable act. They must be eliminated. And to this end we are dedicating ourselves." |
| 30 Jun 1934 |
The killing of John Dillinger promotes Floyd to Public Enemy Number 1. |
| 22 Oct 1934 |
Notorious bank robber Pretty Boy Floyd dies of multiple gunshot wounds in the back, after a shootout with the FBI at the Conkle Farm in East Liverpool, Ohio. |
| 1970 |
Ruby Floyd dies. |


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object mri.v3ds.Track3ds
public class Track3ds
Base class for all keyframer tracks.
| Field Summary | |
|---|---|
static int |
LOCK_X
|
static int |
LOCK_Y
|
static int |
LOCK_Z
|
static int |
LOOP
Track loops. |
static int |
REPEAT
Track repeats. |
static int |
SINGLE
Track runs one and stops. |
static int |
UNLINK_X
|
static int |
UNLINK_Y
|
static int |
UNLINK_Z
|
| Constructor Summary | |
|---|---|
Track3ds()
|
| Method Summary | |
|---|---|
int |
flags()
Get track flags. |
int |
loopType()
Get loop type. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final int SINGLE
public static final int REPEAT
public static final int LOOP
public static final int LOCK_X
public static final int LOCK_Y
public static final int LOCK_Z
public static final int UNLINK_X
public static final int UNLINK_Y
public static final int UNLINK_Z
| Constructor Detail |
|---|
public Track3ds()
| Method Detail |
|---|
public int loopType()
public int flags()
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


The matrix which performs a rotation through angle θ about the origin is: $$\left( \begin{array}{cc} \cos \theta & - \sin \theta \\ \sin \theta & \cos \theta \end{array} \right ) .$$


Report Title
Property Address
Cumulative Results
Cash Needed:
Total
Total
Total Rental Income:
Total
Mortgage Expenses:
Yearly
Total Expenses:
Total
Net Operating Income:
Total
Year One Results
Monthly
Yearly
Income:
Monthly
Yearly
Mortgage Expense:
Monthly
Yearly
Vacancy Expense:
Monthly
Yearly
Property Management:
Monthly
Yearly
Property Tax
Monthly
Yearly
Insurance:
Monthly
Yearly
Maintenance:
Monthly
Yearly
Utility:
Monthly
Yearly
Other:
Monthly
Yearly
Cash Flow:
Monthly
Yearly
« Back
Home
New Report


---
layout: post
title: Dance in Faith
date: 2009-06-06 22:41:07.000000000 +05:30
categories:
- EOS 1000D
- raxeffect
tags:
- Blur
- Dance
- Faith
- long exposure
- Night
- Religion
- Ritual
status: publish
type: post
published: true
meta:
_edit_last: '3'
author:
login: raxeffect
email<EMAIL_ADDRESS>display_name: raxeffect
first_name: Rakshith
last_name: Prasad
---
Av: F/6.3; Tv: 0.067(1/15)s; f: 18mm (35mm-equiv:35 mm); ISO: 800
This photo was taken during a function in my native place. It is called "Bhootha Kola". It was a very pleasing result for me to see the result of the photoshoot for the following reasons -
- Among the things I was trying to achieve during the photoshoot that night, was to catch the rotation of the subject as a blur; that, with the waist accessory flying around defining a circle gives this photo a very dynamic as well as circular motion effect.
- The capture of the flying embers in mid-air from the fire stick was very pleasing.
- The vibrancy of the different colours catches the eye. To get the natural colours in the low lighting conditions, I had to refrain from using the flash. To avoid the image-shake while clicking, I resorted to a high ISO.
Coming to the improvement in this photo -
- I would've liked the face and body of the subject to be defined a little more clearly. But using a long exposure to get the motion blur, this is an expected and accepted side effect.
- The area around the fire stick has a very high exposure and stands out glaringly. A slightly lesser exposure would've helped avoid it. However, the vibrancy and the brightness of the rest of the photo would've been dulled out by this. So, i guess, this is the compromise I had to resort to.


Polo field for SEAG inaugurated today
by Manila Standard SportsThe leadership of the Philippine National Federation of Polo Players yesterday expressed its deep gratitude to three major stakeholders—Philippine Olympic Committee, Philippine SEA Games Organizing Committee and the Philippine Sports Commission—for the fulfillment of its dream to stage its sport in the region’s biggest sporting show.
“Without their support we will not realize our dream of playing in the coming regional meet and play against the top guns of other countries,” said PNFPP founding member and Rep. Mikee Romero.
“Hats off to POC President and Rep. Bambol Tolentino for all the help, without his backing, our sport will be left out in the cold, and of course to House Speaker Alan Peter Cayetano for the valuable support and motivation,” said Romero.
Romero likewise thanked PSC, especially its chairman Butch Ramirez, for helping fund its training in Buenos Aires, Argentina.
“It was a wonderful experience and we learned a lot during our two-week training camp,” said Romero, who is fast recovering from a bad fall during the team’s Argentina training.
Meantime, the majestic ‘Bamboo Pavillon’ of the Miguel Romero Field in Calatagan, Batangas will be blessed and inaugurated today.
Diplomats, high-ranking sports officials and polo enthusiasts are expected to grace the blessing and inauguration.
“Aside from our beautiful facilities, the Bamboo Pavillon will be a great attraction for polo lovers. Designed exquisitely, it really ooks regal,” added Romero.
An added appetizer is the friendly game between Team Philippines against some Argentine and British players.
Polo competitions will start Sunday although the official opening of the biennial meet will be held on Nov. 30 at the massive Philippine Arena in Bocaue, Bulacan.


### hacked?
- not registered?
- not licensed?
- stealing?
- disclosure?
- consent?
### some terminology
- zero day
- malware
- cracker
- virus
- DoS, DDoS
- phishing
- social engineering
- brute force
[insecam.org](https://www.insecam.org/)
a large portion of medical devices and hospital computers
are accessible using default windows usernames and passwords
most hacking remains legal
### some terminology
- 2-factor auth
- SSL
- SSL certificate
- HTTP vs. HTTPS (that s tho)
- IP address
- patch
- domain, DNS
- cookie 🍪
- JavaScript☕📜
always install **patches** ASAP
(take your time on major releases)
don't download things from emails you're not expecting
get a VPN if you use wi-fi not at work or at home
use ~~strong passwords~~ a password manager
(e.g. [1Password](https://1password.com/))
### your password
- shoot for at least 10 characters
- numbers and letters
- avoid dates
- avoid common substitutions (e.g. a ➡ 4, o ➡ 0, t ➡ 7)
- use ridiculous and long passphrases
don't trust ~~free~~ not your wifi
### every website gets your
- timezone
- language
- IP address
- browser and version
- browser plugins
- operating system
- dimensions of your browser window
- cookies
### about those 🍪
- used for storing temporary info
- used to identify and authenticate you
- filtered for just the domain the request is sent to
- used to track most pages you visit by google and facebook
let 'em have it
https://adnauseam.io/
### desktop blocking
![blocking](/images/blocking.jpg)
### mobile blocking
- firefox focus
- iOS: AdBlock
- Android: harder, blockers must be 'side-loaded' (Google is an ad company)
normalize encryption
(use Signal)
support [eff.org](https://eff.org)!
i've got 1.2 billion passwords.
who wants to see if i have theirs?


EpiCrystal Ball
Having trouble picking a track in Epicodus? It can be hard to decide. But don't worry, take this quiz to find out which option is best for you!
Are you leaning towards front end or back end development?
Definitely front end! I love styling and making a user's experience fantastic.
Back end for sure. Who cares about what it looks like when I can make it do so much?
What inspired you to become a developer?
My parents. They always pushed me to get into computers because they are the way of the future! Just like that cliche was the way of the future in 1994.
Peer pressure. All my friends are going into the tech field, and everyone knows that techies are the new lemmings.
Passion.
Stability. I had a lame job before and now I need a decent career that pays enough to live comfortably. Also, the lax dress codes and young culture at most tech companies doesn't hurt.
How long have you been coding?
Many years. I don't even know why I'm enrolled in Epicodus, to be honest.
6 months - 1 year. I have a bit of experience but I still have a lot to learn
2 - 6 months. I have taught myself a little but I am excited to learn a lot more!
0 - 2 months. I have only dinked around with it but don't really know much.
Negative 20 years. I have actually never used a computer. Are floppy disks still a thing? I never know what the kids are into these days.
Do you want to deal with content-heavy websites mostly?
Yes, lay it on me! I want to work with large complex data systems, like the kind found in government work.
No thank you, heavy loads of data aren't my fave.
Would you prefer to work for a larger business such as Microsoft?
That sounds great! I love job security.
No, I'd rather work for a smaller company or a startup. More risk but more reward!
Which Harry Potter character do you most identify with? (Totally relevant...kind of... not really)
Harry! I love being the center of attention but pretending it was unwantingly thrust upon me. Also I can rock glasses like nobody's business.
Hermione! I was bullied when I was younger because people are jerks, but now I am pretty much the best thing since sliced bread, so...
Cormac McLaggen! I never get the attention I deserve, even when I am clearly better than the other options. Like when Ron got picked to be on the Gryffindor quidditch team over me. Everyone always picks Ron over me. I bet most people even thought this spot was going to be Ron and not me! Some people have all the luck.
How do you feel about puns?
A steak pun is a rare medium well done.
Whoever stole my copy of Microsoft Office is in big trouble. You have my Word.
The person who created the first pun died a horrible, horrible death. It was a pretty killer pun.
 Coincidentally, I am no fun.
Speaking of puns:
Property has become so expensive in Southern California that they're beginning to sell yards by the foot.
Even if Freud can be loose in his morals, Immanuel Kant.
Have you noticed that the flu is most common in cities with the heaviest congestion?
That wasn't a question...?
I said I don't like puns! (I am really and truly no fun at all)
Submit


Symbollic Logic
by Lewis Carroll
“Book 1: Chapter 4”
Additional Information
- Year Published: 1896
- Language: English
- Country of Origin: United States of America
- Source: Carroll, L. (1896). Symbollic Logic. New York; Macmillan & Co.
-
Readability:
- Flesch–Kincaid Level: 10.5
- Word Count: 610
- Genre: Informational
- Keywords: math history, mathematics
- ✎ Cite This
Downloads
CHAPTER IV.
NAMES.
The word “Thing”, which conveys the idea of a Thing, without any idea of an Adjunct, represents any single Thing. Any other word (or phrase), which conveys the idea of a Thing, with the idea of an Adjunct represents any Thing which possesses that Adjunct; i.e., it represents any Member of the Class to which that Adjunct is peculiar.
Such a word (or phrase is called a ‘Name’; and, if there be an existing Thing which it represents, it is said to be a Name of that Thing.
[For example, the words “Thing,” “Treasure,” “Town,” and the phrases “valuable Thing,” “material artificial Thing consisting of houses and streets,” “Town lit with gas,” “Town paved with gold,” “old English Book.”]
Just as a Class is said to be Real, or Unreal, according as there is, or is not, an existing Thing in it, so also a Name is said to be Real, or Unreal, according as there is, or is not, an existing Thing represented by it.
[Thus, “Town lit with gas” is a Real Name: “Town paved with gold” is an Unreal Name.]
Every Name is either a Substantive only, or else a phrase consisting of a Substantive and one or more Adjectives (or phrases used as Adjectives).
Every Name, except “Thing”, may usually be expressed in three different forms:-
(a) The Substantive “Thing”, and one or more Adjectives (or phrases used as Adjectives) convey ing the ideas of the Attributes; (b) A Substantive, conveying the idea of a Thing with the ideas of some of the Attributes, and one or more Adjectives (or phrases used as Adjectives) conveying the ideas of the other Attributes; (c) A Substantive conveying the idea of a Thing with the ideas of all the Attributes.
[Thus, the phrase “material living Thing, belonging to the Animal Kingdom, having two hands and two feet” is a Name expressed in Form (a). If we choose to roll up together the Substantive “Thing” and the Adjectives “material, living, belonging to the Animal Kingdom,” so as to make the new Substantive “Animal,” we get the phrase “Animal having two hands and two feet,” which is a Name (representing the same Thing as before) expressed in Form (b). And, if we choose to roll up the whole phrase into one word, so as to make the new Substantive “Man,” we get a Name (still representing the very same Thing) expressed in Form©.]
A Name, whose Substantive is in the plural number, may be used to represent either
(1) Members of a Class, regarded as separate Things; or (2) a whole Class, regarded as one single Thing.
[Thus, when I say “Some soldiers of the Tenth Regiment are tall,” or “The soldiers of the Tenth Regiment are brave,” I an using the Name “soldiers of the Tenth Regiment” in the first sense; and it is just the same as if I were to point to each of them separately, and to say “This soldier of the Tenth Regiment is tall,” “That soldier of the Tenth Regiment is tall,” and so on. But, when I say “The soldiers of the Tenth Regiment are formed in square,” I am using the phrase in the second sense; and it is just the same as if I were to say “The Tenth Regiment is formed in square.”]


I’d thought I would give jekyll a go and found jekyll-bootstrap for blogging.
Great examples on github, especially page.
Blogging like this is great way to get into jekyll, markdown and github pages.
testing... this comes from the post2 templates icon-camera-retro
Reload


In mid-October I began using kratom. For the next two weeks, I used it almost daily, taking 3 grams of crushed premium leaf in the mornings. The effects were subtle and positive. It wasn't as obvious as opiates, but I noticed that my workdays were much more enjoyable, and my boss much more tolerable.
On Halloween weekend I suddenly became very ill and immediately stopped use. I experienced fatigue and an extreme loss of appetite, along with a sharp pain in my abdomen. I could not keep food down for the first week or so. I became extremely jaundiced and, as is common with jaundice, my entire body was itchy.
The diagnosis of hepatitis was made when my ALT (liver enzyme) was measured at 500, which is around ten times normal. Two weeks later after onset this number peaked at around 1400. For completeness, my AST (liver enzyme, 40 is normal) peaked at around 300, and my bilirubin topped out at six (one is normal, this causes jaundice).
I have seen specialists about this illness and a cause (other than kratom) cannot be found. I am an otherwise healthy female, and take no medication beyond the occasional allergy pill and tylenol. Beyond the kratom, I had not consumed anything unusual prior to becoming sick. I do not drink heavily or frequently, and my illicit use of drugs is limited to marijuana (once/month). I've been tested for numerous viruses known to cause hepatitis, and all have come back negative. More chronic causes of hepatitis, such as autoimmune hepatitis and Wilson's disease, have also been ruled out.
I believe, though I cannot prove, that kratom caused this illness. Whether it was the kratom itself or some contaminant on the plant I cannot say (I should note that the vendor from whom I purchased it is reputable). I have learned that there exist viruses that are known to cause hepatitis but are so rare that there aren't test for them, so that is still a possibility. But it was more likely the kratom, as some herbal supplements are well-known for causing liver malfunctions.
It took approximately two weeks for the very acute symptoms to pass, and it was another couple of weeks before I had my energy back. It is now six weeks after the initial illness and I feel fine, though my liver enzymes are still slightly elevated.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Sorry, there are no dropouts from Online College! Maybe they are too busy with
extracurricular activities
to find the time to drop out.
home


About
Hello! I am Rushi Agrawal, a computer technology enthusiast and engineer.
I work as a software consultant and engineering lead at a startup. I have brought two products of that startup to launch as a core engineer. In my previous job, I was the primary contributor for developing relational database service for their infrastructure cloud.
I created and contributed to a few open source projects too.
I have an interest in distributed systems, especially distributed databases. Even though distributed databases find a wide range of application in today’s world, I still think that a vast majority of them can do pretty well with existing relational databases like PostgreSQL.
I graduated from IIT Hyderabad in 2012 with a Bachelors degree in Computer Science and Engineering. Before that, I did my schooling from my hometown Shegaon near Nagpur.
When not thinking about software, I watch and read about cricket.
Contact me via email at<EMAIL_ADDRESS>or via phone at +91 99-4518-4519.
Thank you for your time.


ALPR Events
| MVG3400 - 2021-05-16 18:47:23 |
| LNX2062 - 2021-05-16 17:54:36 |
| HNM7775 - 2021-05-16 17:50:47 |
| NVT9486 - 2021-05-16 17:22:18 |
| CYP2154 - 2021-05-16 17:19:00 |
| CYP2154 - 2021-05-16 17:18:44 |
| HNM7775 - 2021-05-16 16:13:47 |
| BML8752 - 2021-05-16 16:00:09 |
| JNM6594 - 2021-05-16 15:56:29 |
| CYP2154 - 2021-05-16 15:44:25 |
| KFD8481 - 2021-05-16 15:34:07 |
| HKG402 - 2021-05-16 15:27:20 |
| FJZ9024 - 2021-05-16 14:49:19 |
| UNKNOWN - 2021-05-16 14:20:41 |
| DFL5451 - 2021-05-16 13:54:55 |
| HKG4043 - 2021-05-16 13:34:48 |
| NKL0252 - 2021-05-16 13:10:01 |
| JKF2978 - 2021-05-16 12:47:26 |
| UNKNOWN - 2021-05-16 12:39:25 |
| NVT9486 - 2021-05-16 12:33:59 |
| GTC9766 - 2021-05-16 12:06:59 |
| CYP2154 - 2021-05-16 11:34:34 |
| LBH7877 - 2021-05-16 10:53:50 |
| US0T2672 - 2021-05-16 09:55:40 |
| NSX6318 - 2021-05-16 09:40:24 |
| JTP7317 - 2021-05-16 09:27:04 |
| LZW2703 - 2021-05-16 09:23:47 |
| UNKNOWN - 2021-05-16 09:10:45 |
| UNKNOWN - 2021-05-16 07:38:05 |
| UNKNOWN - 2021-05-16 07:38:05 |


Constrained Delegation
Obviously unconstrained delegation can be quite dangerous in the hands of a careless admin. Microsoft realized this early on and released ‘constrained’ delegation with Windows 2003. This included a set of Kerberos protocol extensions called S4U2Self and S4U2Proxy. These extensions also enable something called protocol transition, which we’ll go over in a bit.
In essence, constrained delegation is a way to limit exactly what services a particular machine/account can access while impersonating other users. Here’s how a service account configured with constrained delegation looks in the Active Directory GUI:
The ‘service’ specified is a service principal name that the account is allowed to access while impersonating other users. This is HOST/PRIMARY.testlab.local in our above example. Before we get into the specifics of how this works, here’s how that target object looks in PowerView:
The field of interest is msds-allowedtodelegateto, but there’s also a modification to the account’s userAccountControl property. Essentially, if a computer/user object has a userAccountControl value containing TRUSTED_TO_AUTH_FOR_DELEGATION then anyone who compromises that account can impersonate any user to the SPNs set in msds-allowedtodelegateto. Ben mentions SeEnableDelegationPrivilege being required to actually modify these parameters.


input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
Annotator reference id.
Annotator reference id. Used to identify elements in metadata or to refer to this annotator type
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
Detects negations and transforms them into not_ form
Detects negations and transforms them into not_ form
Requires sentence boundaries to give score in context Tokenization to make sure tokens are within bounds Transitivity requirements are also required
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Overrides annotation column name when transforming
Overrides annotation column name when transforming
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
to be validated
True if all the required types are present, else false
Inspired on vivekn sentiment analysis algorithm https://github.com/vivekn/sentiment/


dpkg-checkbuilddeps(1) dpkg utilities dpkg-checkbuilddeps(1) NAME dpkg-checkbuilddeps - check build dependencies and conflicts SYNOPSIS dpkg-checkbuilddeps [option...] [control-file] DESCRIPTION This program checks the installed packages in the system against the build dependencies and build conflicts listed in the control file. If any are not met, it displays them and exits with a nonzero return code. By default, debian/control is read, but an alternate control filename may be specified on the command line. OPTIONS --admindir=dir Change the location of the dpkg database. The default location is /var/lib/dpkg. -A Ignore Build-Depends-Arch and Build-Conflicts-Arch lines. Use when only arch-indep packages will be built, or combine with -B when only a source package is to be built. -B Ignore Build-Depends-Indep and Build-Conflicts-Indep lines. Use when only arch-dep packages will be built, or combine with -A when only a source package is to be built. -d build-depends-string -c build-conflicts-string Use the given build dependencies/conflicts instead of those con‐ tained in the debian/control file. -a arch Check build dependencies/conflicts assuming that the package described in the control file is to be built for the given host architecture instead of the architecture of the current system. -?, --help Show the usage message and exit. --version Show the version and exit. Debian Project 2012-05-22 dpkg-checkbuilddeps(1)


In JDK 1.5, calling new Long() causes memory allocation. Long.valueOf() is more memory friendly. Example :
public class Foo { private Long i = new Long(0); // change to Long i = Long.valueOf(0); }


When you start learning Spring MVC development configuring
Tomcat server in Eclipse Neon IDE can be a difficult task.
This tutorial will help you setup your development environment.
Installing Tomcat 8.5
Visit Apache Tomcat download page and grab apache-tomcat-8.5.6.tar.gz file.
You can also download file from terminal:
To make things easier you will install Tomcat inside your home directory.
I assume that you already installed Java on your box,
if not now is the time to do this. To instal Java follow tutorial at webupd8.
Let’s check if everything is OK by starting Tomcat and opening
http://localhost:8080 in your favorite browser:
You should see Tomcat welcome page:
Now we will add an administrator account on Tomcat server. This is not needed
by Eclipse but it may come in handy when you will later want to play
with Tomcat:
Now you must add the following line to tomcat-users.xml file:
Here we are creating admin user with 1234 password and we give him (or her)
permissions to administration gui.
Let’s start Tomcat again (using ./catalina.sh run) and check that
we can visit Tomcat manager page (located at http://localhost:8080/manager/html):
Configuring Eclipse Neon
Before we start check that you have following plugins installed:
Now we must create new Server Runtime Environment. Go to Preferences -> Server -> Runtime Environment -> Add…
Then specify path to your Tomcat installation (/home/user_name/tomcat)
and optionally JRE (use Oracle Java 8 if available) and click Finish.
There is no Tomcat 8.5 in my wizard (if you completed previous steps skip this secion)
There is a bug in Eclipse that prevents some users from seeing
Tomcat 8.5 option in their wizards:
The solution is simple, we manually change Tomcat version number say
from 8.5.6 to <IP_ADDRESS>.6 and this will allow us to use newer Tomcat version
with buggy Eclipse.
More details can be found in this SO question.
Following script will will patch Tomcat server:
After running script try to start Tomcat server to check if it is working.
Then if everything is OK create new Runtime Environment in Eclipse using Tomcat 8 option.
Starting Tomcat from Eclipse
Open Servers window (press Ctrl+3 and write in search box Servers).
Use link in Servers window to open New Server wizard and create a new server:
You should see new server created:
Click on server and choose Clean option, then click again and select Start.
If you have Tomcat running in terminal (./catalina.sh run) stop it before
you start Tomcat via Eclipse. Console window should display various
log entries from starting Tomcat. Now click on server and select Stop.
Now it’s time to restart Eclipse and check if everything works after
restart.
Creating simple Spring MVC app
We will create simple Spring MVC application to check if everything is working.
Let’s start by creating new Maven project (New Project -> Maven Project and check Create simple project):
Don’t forget to select WAR as packaging method.
Open your POM file in XML editor and add following lines to it:
Then let’s add four more files to our project:
Now click on pom.xml select Run As -> Maven install, this should
create hellotomcat-0.0.1-SNAPSHOT.war file in target directory.
Click on project select Run As -> Run Configurations, then select Apache Tomcat and press New button.
Click Apply and Close.
Click on project select Run As -> Run on Server:
and Finish.
Go to http://localhost:8080/hellotomcat/ and check if you see greetings.
Now open HomeController file change text and save changes, after a few
seconds refresh browser page - you should see your new text.


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 Joe asked into a borrowed phone.  I told him I’m Brazilian and now all the guy wants to talk about is football. He’s relentless. I think it’s all he knows how to say in English but he obviously doesn’t understand a word I say. He keeps rambling on about Roberto Carlos and some dude named Alex.”
“What?”
“I know, it sounds familiar, but I don’t know what to make of it, man.
Joe didn’t panic. The Wolf. I, on the other hand, was trying to get back to my hostel after spending the day with this guy, whom I’d only met the day before. Our mother’s were walking friends (you know – mothers) and when mine heard I was coming to Istanbul for a week of work, she’d put me in touch with Joe. My kid brother had already met him a few months before on HIS trip to Turkey and completely recommended the guy.
“Oh, you’re going to Istanbul?” The kid had asked me. “You should totally call Joe.”
“Yeah, mom said the same thing. What’s his story?”
“Joe? He’s totally cool, man. Very likable guy, all chill.  It’s awesome. He loves it.”
“Cool, it sounds like he’s my guy. Do you have his number?”
“Yeah, I’ll send it to you.”
He forgot, as it sometimes goes, so I got it from mom. “Oh, you’re going to love Joe!” she said.
“I know. Paul already told me he’s awesome.”
So I’d gotten in touch with him when I arrived in Istanbul and agreed to meet hime between the Blue Mosque and the Ayasofia, also known as the Hagia Sofia, two of the most well-known mosques/churches/museums in Istanbul. We met by the fountain on a very warm and clean-aired day, where people were walking, talking, reading and eating corn, which must be a Turkish thing. In general, people were out enjoying their weekend and judging from all the Turkish flags everywhere, their independence day.
Joe turned out to be the bald guy I’d missed on my first pass around the fountain. Like my brother, he sported the bald look well, though instead of the hideous chops that Paul dons occasionally, Joe had a typical Walnut Creek goatee. His pretty girlfriend, Zeynep, was with him, tanned skin, light hair, light eyes and a girlish figure that attracted the attention of most guys around. I thought she looked more Iranian than Turkish, and I would later learn that I was right about that.
“Hang on. Zeynep will talk to the driver. Give him your phone.”
“Give him my pho… No! Joe! Wait! Are you nuts?”
“Alo”, responded Joe’s girlfriend.
“Shit, hang on, Zeynep. Hey, Ronaldinho! Yo! Driver dude! Yeah, Kaka! Robinho! Woooooh, futbol! Right On! Here: talk to this chick.” I handed him the phone.
They talked for a bit and then I got the phone back with Joe on it.
“Dude, there’s a marathon going through the city right now. It’s cutting off the roads that go back to your part of the city.”
“goddamn health nuts…” I heard myself mumble.
“Wait,” paused Joe. “Didn’t you say you’d run a triathlon last year or something?” Apparently, he wasn’t as drunk as I’d pegged him for earlier that night.
“Well, yeah, but that was like, more than a year ago, Joe. And besides, I had the decency to run in the middle of central California, and the only people I’d disturb were yokels from highway 46 who had no business being out in that heat anyway.”
“Fair enough,” said Joe.
“Right. And that’s not important right now. What should I do? Just get out of the cab and wait?”
“No! Under no circumstances should you get out of the cab. Listen: Zeynep knows where you are. Let the meter run, talk football with the guy. It takes 5 minutes to get to where you are. We’ll be there in 2.”
Zeynep, I should mention, could qualify for most Grand Prix spectacles. A true Turkish driver, she paid no heed to lines on the road, only other cars. And she saw them as obstacles, points to watch for and pass with the greatest expediency. And having grown up in Istanbul, she’d learn to do so better than any man I know, Italian, Brazilian or Turk. I believed him.
And sure enough, before homeboy could get me acquainted with Turkey’s multitude of teams, I saw them pull up behind me. I paid the man and got out of the car, being sure to say ‘fish’ which means he gives me a receipt. Important when you’re traveling on business, you know.
We killed the remaining 20 minutes of the marathon, the mass of people passing over the Bosporus Bridge and through and around the old part of town (old: talk about an understatement!) by having Zeynep drop us off at a Metro somewhere and taking it to the end of the line and back. Joe has good ideas like that.
We eventually got off as Taksim Square, the center of the European side of Istanbul and took a cab from there. I dropped Joe off at his place on the way across the Golden Horn, and thanked him for the evening’s adventures.
“Sorry about the troubles, man. I had no idea about this marathon.”
“Are you kidding me?” I jabbed him.  And with that, we parted ways.
“Wait, one more thing,” Joe warned me. “Watch these guys. Taxi drivers in Istanbul are all run by the Mafia.”
Whatever, I thought. Everybody says that about their cabbies. And I’ve always had a watchful eye in foreign places, which, for me, is everywhere.
“Nonsense,” I said. “This is a good man.”
“No, seriously. Watch the meter. This shouldn’t cost more than 50 Turkish lira.”
“Okay!” I shouted out, not paying much attention. “Thanks for the fun day, man. I’ll call you next week!”
And with that, I was off to Asia.
–
The ride took me up and around what must be every corner of the center of the world. In the distance, when we cleared a hill and I could see the ocean, the tankers of the world floated on, some moving and some anchored. Lights flickered everywhere and there was little movement off the road.
On the road was another thing altogether. Cars zoomed passed us and we passed them. Stone walls were a blur on every side, and the poorly kept Constantine roads of stone occasionally showed through the asphalt, and that’s when we’d really rock. At 160 kph on city highways, almost anything can happen. When we finally crossed the Bosporous Bridge over to Asia, I almost didn’t see it for fear of my life over that busy waterway.
But despite the driver’s confident nod when I gave him the address of the corporate hotel and said, “Okay?”, the drive took far longer than I was expecting. And went through far darker and more remote-looking areas than I cared to expect.
“Relax, Pete,” I told myself. “This is just a sensation of disorientation that happens every time you’re in a country you know nothing about. Remember Prague?”
And I did, I did!
–
Deep into the Asian side of Istanbul now, my worries took more form – my fear took more shape. Where am I? Where is he taking me? Does he know?
…
Do I? There are silent and square concrete buildings at every line of sight. I’ve wandered into the 3D version of Lewis Carol’s mind. Wonderful. My mind was tired and the shisha we’d smoked earlier was mixing with the beer. Things were getting strange, but could I maintain? Would I make the sanity last? That was the real question.
What am I doing here? Few people in my life even know on what continent I’m ON right now. Let alone where I am.  What hope is there that others do?
All good questions, I thought to myself, once I got a grip. But now was not the time for their answers. We’d passed a few closed rug shops and kabob stands that went long into the night, reminding me of Saudi Arabia and the fact that Iraq was literally touching the same country I was in, and that reassured my position on the globe, reminding me that things here would not, no matter what, resemble what I could possibly expect. Or manage to explain.
And despite all the wandering, despite all the wondering, he suddenly pulled into an intersection and realized he wasn’t on the right track. My body jerked forward, knocking me out of my reverie and causing me to lean just enough towards the front of the car that I could see the Hotel’s name in the distance.
“There! There!”  “There it is!”
I’d lost it, clearly. Joe had told me to watch the meter, to pay attention to this guy, and here I was, slapping the seat like a giddy child, hoping the nice man would just take me where I was supposed to be. This was more than I had expected. But the incident brought me to my senses a bit and by the time he’d pulled against the hotel’s sidewalk, I had my wits back about me. I think.
“Hundred fiftin,” he said, pointing to the meter. I looked at it intently. Sure enough, there it sat, reading 115.24.
‘This shouldn’t cost more than 50 Turkish lira,’ Joe had said. But how could I challenge him now? I didn’t know these things! Besides, my employer was paying for it and I was in no mood or condition to argue or even haggle with a cabbie. Especially not in Turkish, and I could hardly expect that this schmo was going to do me the favor of suddenly bursting forth with Anglo-sounding discourse. ‘Hundred fiftin’ it was.
I reached back for my wallet and remembered that I had 4 50′s in there, with the spare 5′s crumpled uncomfortably in my front pocket. I pulled 2 50′s from my wallet and then grabbed the glob of crumpled 5′s. I was pretty sure that I had 3 5′s and 2 50′s in my hand when I shoved it towards the front of the car and said, “fish?”
He hesitated and then said, “No, sir – hundred fiftIN!” and showed me 4 5′s and 1 50 in his hand.
“Really?” my mind wondered. I was sure I’d grabbed 2 50′s out of my wallet, so I checked again, while he pestered me, saying “No, sir – hundred fiftIN! Hundred fiftIN!”
 I stumbled into the hotel with a receipt and before falling hard on that bed, barely remembering to set the alarm for work the next day, went over the bills that I had on my person, in all my pockets.
I could not account for the missing 50.
–
The next morning on the way to work, one of my colleagues warned me about taxi drivers in Turkey, and how they are very good with their hands, changing bills between 5′s and 50′s, which happen to look very much alike.
“Bunch of thieves, eh?” He said to me.
“I know,” I said. “I’ve heard.”
Wombat’s Terrace, Berlin, Germany – September 2008


{% extends "layout.html" %} {% import "pagination.html" as p %} {% block body %}
{{ entry.timestamp.strftime('%Y-%m-%d %H:%M') }}
{{ entry.text|safe }}
Miniblog is a single file blog powered by Flask + SQLAlchemy + Bootstrap. No other extensions are used and there are more funtions to come.


Given an array consisting of only positive integers and a target sum.
Generate all the combinations from the array that sum up to the given target sum.
Example:
a = [2, 4, 6, 8]
target = 8
ans = [(2, 6), (2, 2, 2, 2), (2, 2, 4), (8,), (4, 4)]
If we observe, we can formulate the following pattern for the problem.
At any point, either a number has to included in the patter or not included.
If it's included, it'll be subtracted from the required sum, otherwise required sum will remain the same and we'll move forward.
The above approach can be consolidated in the following algorithm:
1. Start with the first element of the array.
2. For all index i.
3. If we included the element: then solve recursively for (sum - a[index], index)
4. If we decide not to include this element, then solve recursively for (sum, index)


CSPICE_FRINFO retrieves the minimal attributes associated with a
frame needed for converting transformations to and from it.
Given:
frcode a SPICE ID for some reference frame.
[1,n] = size(frcode); int32 = class(frcode)
the call:
[cent, clss, clssid, found] = cspice_frinfo( frcode )
returns:
cent the SPICE body ID for the center of the reference frame
(if such an ID is appropriate).
[1,n] = size(cent); int32 = class(cent)
clss the class ID or type of the frame. This identifies which
subsystem will perform frame transformations.
[1,n] = size(clss); int32 = class(clss)
clssid the ID used for the frame within its class. This may be
different from the frame ID.
[1,n] = size(clssid); int32 = class(clssid)
found flag returning true if 'cent', 'frclss' and 'frcode' are
available, false if not.
[1,n] = size(found); logical = class(found)
'cent', 'clss', 'clssid' and 'found' return with the same
vectorization measure (N) as 'frcode'.
Any numerical results shown for this example may differ between
platforms as the results depend on the SPICE kernels used as input
and the machine specific arithmetic implementation.
%
% Retrieve frame information for a scalar 'code'.
%
disp('Scalar' )
code = 13000;
[cent, clss, clssid, found] = cspice_frinfo( code );
fprintf(' code center class class_ID found\n' );
fprintf( '%d %d %d %d %d\n', ...
code, cent, clss, clssid, int32(found) );
%
% Retrieve frame information for a vector of 'codes'.
%
disp('Vector' )
codes = [1:5];
[cent, clss, clssid, found] = cspice_frinfo( codes );
fprintf( 'code center class class_ID found\n')
fprintf( '%d %d %d %d %d\n', ...
[codes; cent; clss; clssid; int32(found) ] );
MATLAB outputs:
Scalar
code center class class_ID found
13000 399 2 3000 1
Vector
code center class class_ID found
1 0 1 1 1
2 0 1 2 1
3 0 1 3 1
4 0 1 4 1
5 0 1 5 1
This is a low level routine needed by state transformation
software to transform states and attitudes between different
reference frames.
The routine first examines local "hard-coded" information about
reference frames to see if the requested frame belongs to this
set. If it does that information is returned.
If the requested information is not stored locally, the routine
then examines the kernel pool to see if the requested information
is stored there. If it is and has the expected format, the data
is retrieved and returned.
For important details concerning this module's function, please refer to
the CSPICE routine frinfo_c.
MICE.REQ
FRAMES.REQ
-Mice Version 1.0.0, 12-MAR-2012, EDW (JPL), SCK (JPL)
fetch reference frame attributes
|


The chrome.pushMessaging
API was deleted in Chrome 43. This
document exists for posterity only.
Use the new Google Cloud Messaging.
Last modified: March 31, 2013
By using the Google Cloud Messaging for Chrome API,
you consent to be bound by the Google APIs Terms of Service ("API ToS"
) at
http://developers.google.com/terms.
You also agree that you and any API Client you create may only access the getChannelId functionality of this API for your own development and personal uses.


{% assets "common_css" %}
{% endassets %}
404
Sorry, that page doesn't exist.
Want to
go home
instead?


{% include head.html %}
{% include sidebar.html %}
{{ site.title }}
{{ site.tagline }}
{{ content }}
{% if site.enable_comment == true %}
Please enable JavaScript to view the
comments powered by Disqus.
{% endif %}


Russia opens investigation into space center fraud after Putin rebuke
The announcement came less than two weeks after President Vladimir Putin complained to government officials about corruption at the facility and called for further investigations
Russian investigators said on Thursday they had opened two criminal cases into the management of a company involved in building the Vostochny Cosmodrome, a space centre in the country's the Far East.
The announcement came less than two weeks after President Vladimir Putin complained to government officials about corruption at the facility and called for further investigations.
Construction of the Vostochny Cosmodrome began in January 2011, part of a plan for Russia to reduce its dependency on the Baikonur Cosmodrome in Kazakhstan, which Russia leases from the former Soviet Republic for space operations.
But Putin told government officials earlier this month he was dismayed by the level of graft there.
"Hundreds of millions have been stolen. Hundreds of millions. Several dozen criminal cases have already been initiated. There have already been court decisions, people are in prison," said Putin.
His spokesman, Dmitry Peskov, said later that 11 billion roubles ($172.45 million) of a total 91 billion roubles allocated to the project had been stolen.
Investigators said on Thursday that they were looking into allegations that fraudulent financial documents had led to the theft of 240 million roubles ($3.8 million) between November 2018 and May 2019.
Top officials at the Cosmodrome said they were in control of the situation and that any violations would immediately be passed to law enforcement officers, the RIA news agency reported.
The Vostochny Cosmodrome, whose name comes from the Russian word meaning 'Eastern', is situated in the Amur region on the border with China.


--- layout: page title: About me subtitle: "More about me" css: "/css/aboutme.css" ---
My name is Mpho Mphego. An Electronic Engineer turned Software Engineer (Now Data Engineer) with a passion for automation, innovation and engineering.
I have been working with Python professionally since 2013 (8+ years), primarily focusing on backend development and currently employed by Capitec Bank
as a Data Engineer.
I was previously employed by SKA (Square Kilometer Array) aka SARAO (South African Radio Astronomy Observatory) as a Senior Software Engineer.
Prior to that I was part of the Marion Island expeditio, you can read up about my expedition experience here : Marion Island 71st Expedition
I created this blog to keep track of all if not most of the things I'm learning. In the past couple of years or so, I started to learn a lot about different aspects of software development. I wanted to be able to track my progress and accomplishments. My goal is to, at least post something twice in a month. Hopefully I can even improve on that. I write about what I am currently learning, things that interest me by doing that I ensure that the knowledge I gain is fully retained and shared to anyone that shares the same interest as me.
My personal and work projects can be accessed on my GitHub page. My personal blog page is a very good place to see some of my work and current things I am learning.
I also have an old Wordpress blog, and most of the posts have been migrated to blog.mphomphego.co.za, I have left some old travelling documented trips to Marion Island, that I'll leave such that I can have a reference to it somewhere.
Should you want to link up with me for work, you are welcome to use these options below
You can email me or find me on LinkedIn if you want to get in touch. As I said, I love meeting new people so don't hesitate to send a message!
If you'd like to set up a meeting with me, drop me a mail and we will schedule a meeting based on your availability.


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Issue: GET-SETF-METHOD-ENVIRONMENTReferences: GET-SETF-METHOD (CLtL p 187)
Category: Change
Edit History: Version 1 9-Jan-87, Version 1 by Masinter
(no version) 7-Apr-87, merged with ENVIRONMENT-ARGUMENTS
Version 2 29-May-87, extracted again
Version 3 5-Jun-87, by Masinter
Version 4 11-Jun-87, for release
Version 5 13-Jul-87, by Masinter
Problem Description:
If a macro that performs similar processing to SETF uses GET-SETF-METHOD,
and that macro occurs within a MACROLET, the expansion will not see the
MACROLET definition, e.g.,
(defmacro special-incf ... (get-setf-method ...) ...)
then
(macrolet ((test (x) `(car ,x)))
(special-incf (test z)))
would not "see" the test definition.
Proposal (GET-SETF-METHOD-ENVIRONMENT:ADD-ARG):
Add an optional environment argument to GET-SETF-METHOD and
GET-SETF-METHOD-MULTIPLE-VALUE. If the argument is not supplied, it
defaults to the null lexical environment. NIL can also be passed explicitly
to denote the null lexical environment.
Allow &ENVIRONMENT variable to appear in the lambda-list subform of a
DEFINE-SETF-METHOD form, as with a DEFMACRO.
Note that macros defined with DEFINE-MODIFY-MACRO correctly pass the
environment to GET-SETF-METHOD.
Clarify that, within the scope of a MACROLET, FLET and LABELS, global SETF
definitions of the name defined by the MACROLET, FLET or LABELS do not
apply. A SETF method applies only when the global function binding of the
name is lexically visible. All of the built in macros of Common Lisp
(SETF, INCF, DECF, POP, ROTATEF, etc.) which modify location specifications
obey this convention.
Test Case:
;;; This macro is like POP
(defmacro xpop (place &environment env)
(multiple-value-bind (dummies vals new setter getter)
(get-setf-method place env)
`(let* (,@(mapcar #'list dummies vals) (,(car new) ,getter))
(setq ,(car new) (cdr ,(car new)))
,setter)))))
(defsetf frob (x) (value)
;;; The following will modify (cdr z) and not (car z)
(macrolet ((frob (x) `(cdr ,x)))
(xpop (frob z)))
;;; The following is an error; an error may be signaled at macro expansion
time
(xpop (frob z)))
Rationale:
This was an omission in the original definition of CLtL.
Current Practice:
Many Common Lisp implementations already have this extension, although some
do not. One implementation has extended GET-SETF-METHOD to take an optional
argument which is incompatible with this use.
Cost to implementors:
Some implementations will have to add this feature, although it is not a
major change.
Cost to users:
This is generally an upward compatible change. In implementations which did
not already take into account the lexical environment for SETF'd forms
might start working differently if the internal implementation of SETF is
changed. The likelihood of this affecting a user's program is very small.
Benefits:
This change improves portability and the ability to use MACROLET, FLET and
LABELS in portable code which might also have SETF forms.
Aesthetics:
SETF methods cannot work correctly within lexically defined function
symbols without this change. This change makes the language more consistent
and correct.
Discussion:
The cleanup committee generally supports this change.
A number of additional changes for rationally dealing with lexical
environments as first class objects, including a more general set of
accessors and constructors for lexical environments is required for many
language extensions (e.g., a portable version of the proposed Common Lisp
Object System) and should be addressed by a future proposal. For a while,
the cleanup committee attempted to deal with these issues together, but
decided to separate them out into their component parts. This issue is the
simplest.


NEWS.md
Ensembles
modeltime.ensemble
, a new R package designed for forecasting with ensemble models.New Workflow Helper Functions
add_modeltime_model()
- A helper function making it easy to add a fitted parsnip or workflow object to a modeltime tablepluck_modeltime_model()
& pull_modeltime_model()
- A helper function making it easy to extract a model from a modeltime tableImprovements
?prophet_boost
prophet_reg()
can now have regressors controlled via set_engine()
using the following parameters:
regressors.mode
- Set to seasonality.mode
by default.regressors.prior.scale
- Set to 10,000 by default.regressors.standardize
- Set to “auto” by default.Data Sets
Modeltime now includes 4 new data sets:
m750
- M750 Time Series Datasetm750_models
- 3 Modeltime Models made on the M750 Datasetm750_splits
- An rsplit
object containing Train/test splits of the M750 datam750_training_resamples
- A Time Series Cross Validation time_series_cv
object made from the training(m750_splits)
Bug Fix
plot_modeltime_forecast()
fix issue with “ACTUAL” data being shown at bottom of legend list. Should be first item.Forecast without Calibration/Refitting
Sometimes it’s important to make fast forecasts without calculating out-of-sample accuracy and refitting (which requires 2 rounds of model training). You can now bypass the modeltime_calibrate()
and modeltime_refit()
steps and jump straight into forecasting the future. Here’s an example with h = "3 years"
. Note that you will not get confidence intervals with this approach because calibration data is needed for this.
# Make forecasts without calibration/refitting (No Confidence Intervals) # - This assumes the models have been trained on m750 modeltime_table( model_fit_prophet, model_fit_lm ) %>% modeltime_forecast( h = "3 years", actual_data = m750 ) %>% plot_modeltime_forecast(.conf_interval_show = F)
Residual Analysis & Diagonstics
A common tool when forecasting and analyzing residuals, where residuals are .resid = .actual - .prediction
. The residuals may have autocorrelation or nonzero mean, which can indicate model improvement opportunities. In addition, users may which to inspect in-sample and out-of-sample residuals, which can display different results.
modeltime_residuals()
- A new function used to extract out residual informationplot_modeltime_residuals()
- Visualizes the output of modeltime_residuals()
. Offers 3 plots:
TBATS Model
Use seasonal_reg()
and set engine to “tbats”.
seasonal_reg( seasonal_period_1 = "1 day", seasonal_period_2 = "1 week" ) %>% set_engine("tbats")
NNETAR Model
Use nnetar_reg()
and set engine to “nnetar”.
model_fit_nnetar <- nnetar_reg() %>% set_engine("nnetar")
Prophet Model - Logistic Growth Support
prophet_reg()
and prophet_boost()
:
growth = 'logistic'
and one or more of logistic_cap
and logistic_floor
to valid saturation boundaries.changepoint_num
, changepoint_range
, seasonality_yearly
, seasonality_weekly
, seasonality_daily
, logistic_cap
, logistic_floor
combine_modeltime_tables()
- A helper function making it easy to combine multiple modeltime tables.update_model_description()
- A helper function making it easier to update model descriptions.modeltime_refit()
: When modeltime model parameters update (e.g. when Auto ARIMA changes to a new model), the Model Description now alerts the user (e.g. “UPDATE: ARIMA(0,1,1)(1,1,1)[12]”).
modeltime_calibrate()
: When training data is supplied in a time window that the model has previously been trained on (e.g. training(splits)
), the calibration calculation first inspects whether the “Fitted” data exists. If it iexists, it returns the “Fitted” data. This helps prevent sequence-based (e.g. ARIMA, ETS, TBATS models) from displaying odd results because these algorithms can only predict sequences directly following the training window. If “Fitted” data is being used, the .type
column will display “Fitted” instead of “Test”.
actual_data
reconciliation strategies when recipe removes rows. Strategy attempts to fill predictors using “downup” strategy to prevent NA
values from removing rows.modeltime_accuracy()
: Fix issue with new_data
not recalibrating.
prophet_reg()
and prophet_boost()
- Can now perform logistic growth growth = 'logistic'
. The user can supply “saturation” bounds using logistic_cap
and/or logisitc_floor
.
seasonal_decomp()
has changed to seasonal_reg()
and now supports both TBATS and Seasonal Decomposition Models.prophet_reg()
& prophet_boost()
: Argument changes:
num_changepoints
has become changepoint_num
modeltime_forecast()
: Now estimates confidence intervals using centered standard deviation. The mean is assumed to be zero and residuals deviate from mean = 0.parsnip
0.1.2.prophet_boost()
: Set nthreads = 1
(default) to ensure parallelization is thread safe.


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